flow 140 by ramine tinati

Flow140: Tracing the Flow of Conversations Ramine Tinati – rt506@ecs.soton.ac.uk – Web and Internet Science – University of Southampton

Research Overview

Social   Networks   such   as   Twi2er   offer   a   pla5orm   for  individuals   to   create   and   share   messages,   establish  ‘friendships’  between  each  other,  and  even  become  part  of  specific  communiAes.      Twi2er  has  enabled  a  range  of  important  social  acAvity  to  succeed,   including   idenAfying   public   health   issues   and  more   recently,   as   a   pla5orm   for   social   and   poliAcal  change.   However,   in   spite   of   this,   the   volumes   of  messages   that   are   transmi2ed   per   day  make   idenAfying  valuable   content   from   the   back   cha2er   and   ulAmately,  influenAal  individuals  from  spam,  difficult.      Flow140   enables   exploraAon   into   the   dynamics   of  conversaAons   between   users   within   a   Twi2er   network  in   real-­‐Ame   or   using   historic   data.   Problems   with  analyzing   and   visualizing   such   large   amounts   of  informaAon  are  overcome  a  filtering   soluAon  based  on  characterisAcs   that   individuals   exhibit   within   the  network,  and  the  flow  of  a  conversaAon.  

The Classification of Twitter Users

Flow140  was  based  upon  the  desire  to  be  able  to  visualize  a  growing  network  of  communicaAons  within  the  Twi2er  service.      IniAal  Project  Aims:  •  Can  we  capture  how  valuable  informaAon  flows  in  a  

conversaAon?  •  Can  we  iden*fy  specific  characterisAcs  of  individuals?  •  Can  we  visualise  it?    Tracing  the  flow  of  informaAon  based  on  specific  criteria:  •  Where  does  the  valuable  informaAon  originate  from?  

•  The  Idea  Starter  

•  How  does  the  informaAon  flow  in  a  conversaAon?  •  The  Amplifier  

•  How  can  valuable  informaAon  cross  streams?  •  The  Curator  

Demonstration: – Filtered vs. Unfiltered Retweet Conversation Stream

Fig 1. Unfiltered Retweet Network

(a) Retweet Network as of Nov 3rd 2011

(b) Retweet Network as of Nov 6rd 2011

(c) Retweet Network as of Nov 9rd 2011

(c) 21:00 Nov 8rd 2011

(d) 09:00 Nov 9rd 2011

(e) 21:00 Nov 9rd 2011

(a) 21:00 Nov 3rd 2011

Fig 2. Filtered Retweet Network

Figure  1  represents  the  retweet  network  for  #nov9  as  a  ‘flat’  network,  in  its  original,  unfiltered  state.    Figures  1   a-­‐c   are   visualizaAons   of   the   network   of   individuals  (the  nodes)  and  the  retweets  (the  edges)  showing  the  dynamic   growth  of   the  network.   This   unfiltered   state  provides   very   li2le   observaAonal   informaAon   or  analyAcal  detail.      Even   during   the   early   stage   of   the   conversaAons  (Fig1.a)   the   idenAfying   the   flow   of   informaAon   is  difficult   and   the   rapid   growth  of   the   communicaAons  makes   it   difficult   to   idenAfy   specific   individuals   and  their   roles.   For   example   the   network   clusters   that  were   idenAfiable   before   the   protest   on   the   3rd  November  become  impossible  to  disAnguish  by  the  9th  November.    

Figure   2   shows   how   our   tool’s   filtering   algorithm  reduces   the   complexity   of   the   network   by  concentraAng   on   the   communicaAons   between  individuals   exhibiAng   certain   network   characterisAcs  of   interest   (e.g.   retweeAng)   to   handle   these   data   at  scale      Figures   2   a-­‐d   represent   the   growth   of   the     same  network   as   Figure   1,   but   this   Ame     with   the   filter  applied,  providing  a  clearer  view  of   the  structure  and  growth   of   the   network.   First   observaAons   of   the  network   idenAfy   a   number   of   users   whose   presence  was   constant   throughout   the   growth   of   the  conversaAons.   These   users,   represented   by   the   red  nodes,   are   individual   that   iniAated   the   conversaAons  that   led   to   the   flow   of   informaAon;   the   node   size   is  determined   by   the   number   of   Ames   they   were  iniAators.